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學生對 阿尔伯塔大学 提供的 Sample-based Learning Methods 的評價和反饋

512 個評分
102 條評論


In this course, you will learn about several algorithms that can learn near optimal policies based on trial and error interaction with the environment---learning from the agent’s own experience. Learning from actual experience is striking because it requires no prior knowledge of the environment’s dynamics, yet can still attain optimal behavior. We will cover intuitively simple but powerful Monte Carlo methods, and temporal difference learning methods including Q-learning. We will wrap up this course investigating how we can get the best of both worlds: algorithms that can combine model-based planning (similar to dynamic programming) and temporal difference updates to radically accelerate learning. By the end of this course you will be able to: - Understand Temporal-Difference learning and Monte Carlo as two strategies for estimating value functions from sampled experience - Understand the importance of exploration, when using sampled experience rather than dynamic programming sweeps within a model - Understand the connections between Monte Carlo and Dynamic Programming and TD. - Implement and apply the TD algorithm, for estimating value functions - Implement and apply Expected Sarsa and Q-learning (two TD methods for control) - Understand the difference between on-policy and off-policy control - Understand planning with simulated experience (as opposed to classic planning strategies) - Implement a model-based approach to RL, called Dyna, which uses simulated experience - Conduct an empirical study to see the improvements in sample efficiency when using Dyna...



Jan 10, 2020

Really great resource to follow along the RL Book. IMP Suggestion: Do not skip the reading assignments, they are really helpful and following the videos and assignments becomes easy.


Oct 03, 2019

Great course! The notebooks are a perfect level of difficulty for someone learning RL for the first time. Thanks Martha and Adam for all your work on this!! Great content!!


1 - Sample-based Learning Methods 的 25 個評論(共 100 個)

創建者 Kaiwen Y

Oct 02, 2019

I spend 1 hour learning the material and coding the assignment while 8 hours trying to debug it so that the grader will not complain. The grader sometimes insists on a particular order of the coding which does not really matter in the real world. Also, grader inconsistently gives 0 marks to a particular part of the problem while give a full mark on other part using the same function. (Like numpy.max) However, the forum is quite helpful and the staff is generally responsive.

創建者 JDH

Sep 23, 2019

Rating 4.3 stars – so far (first two classes combined)

Lectures: 4.0stars

Quizes: 4.0stars

Programming assignments: 4.5stars

Book (Sutton and Barto): 4.5stars

In the spectrum from the theoretical to practical where you have, very roughly,...

(1) “Why”: Why you are doing what you are doing

(2) “What”: What you are doing

(3) “How”: How to implement it (eg programming)…

...this is a “what-how” class.

To cover the “why-what” I strongly recommend augmenting this class with David Silver’s lectures (on Youtube) and notes from a class he gave at UCL. This covers more of the theory/math behind RL but covers less on the coding. Combined together with this class it probably comprises the best RL education you can get *anywhere*, creating a 5-star combo.

創建者 Ivan S F

Sep 29, 2019

Great course. Clear, concise, practical. Right amount of programming. Right amount of tests of conceptual knowledge. Almost perfect course.

創建者 Manuel B

Nov 28, 2019

Great course! Really powerful but simply ideas to solve sequential optimization problems based on learning how the environment works.

創建者 Manuel V d S

Oct 04, 2019

Course was amazing until I reached the final assignment. What a terrible way to grade the notebook part. Also, nobody around in the forums to help... I would still recommend this to anyone interested, unless you have no intention of doing the weekly readings.

創建者 Maxim V

Jan 12, 2020

Good content, but a lot of annoying issues with grader.

創建者 hope

Jan 25, 2020

This course is ok if you're reading the Sutton & Barto RL book and would like to have some quizzes to follow along. The programming assignments are not really "programming" because you're constrained to type a handful of lines in a few places into a solution that is largely has been prepared for you. With "hints" like "# given the state, select the action using self.choose_action_egreedy(),

# and save current state and action (~2 lines)

### self.past_state = ?

### self.past_action = ?"

it is impossible to get them wrong. These exercises are ok as labs (comparing various algorithms, etc), but the programming part can be done by rote. Coursera has classes with more intense and creative programming assignments and the learning there seems to be much deeper.

創建者 Andrew G

Dec 24, 2019

The course needs more support and / or error message output for the programming assignments. Code that seems correct can easily fail the autograder, and the only method of recourse is posting in the forums, which may or may not be received by a moderator.

創建者 Bernard C

Mar 22, 2020

Course was good but assignment graders were terrible.

創建者 Juan C E

Mar 07, 2020

Many mistakes with grading and 100% penalty applied for tasks not completed on time, when the rules say that you can submit your assignments and do your quizzes after the deadlines without any penalty.

創建者 Maximiliano B

Feb 23, 2020

The second course of the specialization is excellent and it provides a solid foundation on sample-based learning methods. The book and the videos complement each other making the learning experience rich and pleasant. The professors explain the content very well and the programming assignments are very interesting to consolidate the knowledge. I had a few issues with the grader and it just returns the score without any message that could help find out what is causing the unexpected behavior. As a suggestion, I would like to suggest that the grader could return any additional information and/or include new unit tests. I am looking forward to begin the next course of the specialization.

創建者 Jonathan

May 10, 2020

Very good class. Has much of the same qualities as its predecessor in the specialization. The methods you learn about though are more exciting, since they go beyond the introductory academic stuff that is not really used in production. I can easily see how Q-Learning, SARSA, and DynaQ architectures are usable in the real world.

Programming assignments are also very similar, but just a **little** bit more challenging. Each assignment has just a touch less handholding than the one before it, although there's still a lot of boilerplate included.

Looking forward to the next class!

創建者 Mukund C

Mar 17, 2020

Excellent Course!! Reading the content and making notes ahead of time before watching the lectures is a MUST!!. The graphics/visuals in describing the concepts in the lectures were very good, especially for a visual learning such as myself. However, I wish there were a few more lectures and the lectures were a little - maybe another 3-5 minutes longer and delved into the derivations/concepts - for example - Bellman Equations to Sarsa/Q-learning/TD.

創建者 Yover M C C

Apr 22, 2020

Excelente curso, la calidad de las lecturas y tareas de programación son muy buenas, un curso que no solo te ayuda a mejorar tus habilidades matemáticas y de programación en el tema de aprendizaje por refuerzo, sino también a entender parte del proceso de aprendizaje mediante TD. Un curso que se disfruta mucho! Gracias!.

創建者 Alberto H

Oct 28, 2019

A great step towards the acquisition of basic and medium complexity RL concepts with a nice balance between theory and practice, similar to the first one.

[Note: the course requires mastering the concepts of the first one in the specialization, so don't start here unless you're sure you master its contents.]

創建者 Parsa V

Jan 06, 2020

This course is perfect.

You will learn everything about sample-based RL. The programming assignments are harder than the previous course, but you will understand all the algorithms better.

These two courses covered part 1 of the book, and you will build a strong foundation of RL for the future.

創建者 Surya K S

Apr 12, 2020

One of the more technically challenging courses I've done. Extremely fulfilling, a very good course to go along with the Book - "Reinforcement Learning, An Introduction". The assignments are very engaging, same with the videos, concise and to the point. The forums were incredibly useful.

創建者 Mark J

Sep 23, 2019

In my opinion, this course strikes a comfortable balance between theory and practice. It is, essentially, a walk-through of the textbook by Sutton and Barto entitled, appropriately enough, 'Reinforcement Learning'. Sutton's appearances in some of the videos are an added treat.

創建者 Damian K

Oct 05, 2019

Great balance between theory and demonstration of how all techniques works. Exercises are prepared so it is possible to focus on core part of concepts. And if you will you can take deep dive into exercise and how experiments are designed. Very recommended course.

創建者 Yoel S

Apr 19, 2020

Interesting topic, medium-advanced material, loved the programming assignments (despite technical difficulties with submissions), the textbook is excellent, and the online course is well organized, balanced, and well presented. Thank you to the whole team!

創建者 Majd W

Dec 06, 2019

One of the amazing things this specialization stands out in is that it is based on a textbook. if you read from it and watch the lectures, you will have a very good understanding of the material. Also, the programming assignments are very beneficial.

創建者 Jesse W

May 14, 2020

This is an excellent course in reinforcement learning. They provide a PDF for a textbook which is very clear and readable, and the lectures do a great job at reinforcing the concepts. The programming assignments are pretty interesting as well.

創建者 AhmadrezaSheibanirad

Nov 10, 2019

This course doesn't cover all concept of Sutton book. like n-step TD (chapter7) or some Planning and Learning with Tabular Methods (8-5, 8-6, 8-7, 8-8, 8-9, 8-10, 8-11), but what they teach you and cover are so practical, complete and clear.

創建者 LUIS M G M

Nov 22, 2019

Great course!!! Even better than the 1st one. I tried to read the book before taking the course, and some algorithmics have not been clear to me until I saw the videos (DynaQ, DynaQ+). Same wrt some key concepts (on vs off policy learning).

創建者 David R

Dec 10, 2019

Course is not easy, videos presentation is a bit dull - but the material is cool and interesting, and the additional quizzes, videos and especially notebooks make it a great course - you learn a lot and see progress. Highly recommended.